Inferring Host Gene Subnetworks Involved in Viral Replication

Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.系统性全基因组功能丧失实验（genome-wide loss-of-function experiments）可用于鉴定在宿主细胞内直接或间接促进或抑制病毒复制的宿主因子（host factors）。本文提出一种结合整数线性规划（integer linear program）与扩散核方法（diffusion kernel method）的计算框架，用于推断上述宿主因子调控病毒复制的分子通路。该方法的输入为两类数据：一是在单宿主基因突变体（single-host-gene mutants）中观测得到的病毒表型（viral phenotypes）集合，二是由多种宿主细胞内相互作用（intracellular interactions）构成的背景网络（background network）。其输出为一组集成子网（ensemble of subnetworks），可对观测到的病毒表型给出一致性解释，同时预测尚未经过实验验证的宿主因子是否参与病毒调控，并鉴定出与病毒存在最直接相互作用的宿主因子。本研究利用筛选酵母基因组（yeast genome）中调控两种RNA病毒（RNA viruses）复制的基因所得到的实验数据，推断宿主-病毒相互作用子网。由于暂无金标准网络（gold-standard network）可供参考，本研究通过计算分析与定性实验验证两种方式对预测得到的子网进行评估。我们开展了一项交叉验证（cross-validation）实验：将部分预留测试基因作为验证集，预测其对病毒复制是否存在调控作用。相较于多个基准模型，本方法能够以更高的置信度做出准确预测，其性能与表现最优的基准模型相当——后者无法推断调控机制通路。本研究同时对本方法生成的两类预测结果展开验证：一是鉴定与病毒组分存在最直接相互作用的宿主因子，二是预测尚未经过实验验证的宿主基因是否参与病毒复制过程。多项预测结果得到了近期独立实验数据的支持，或属于已被证实的相关复合物或通路的组分与功能协同因子。本研究所用的整数线性规划代码、背景网络数据以及推断得到的宿主-病毒相互作用子网，均可通过以下网址获取：http://www.biostat.wisc.edu/~craven/chasman_host_virus/。